Skip to content

A
ags_grabber
Commit 743c17e8 authored by Bryson Howell's avatar Bryson Howell
Browse files
Options

Cleaned up terrain collection code

parent bb787a40
Hide whitespace changes
Inline Side-by-side
Showing with 262 additions and 167 deletions
feature_set.py
View file @ 743c17e8
...@@ -82,7 +82,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he ...@@ -82,7 +82,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
query_cnt = 0 query_cnt = 0
while type(q)==list and query_cnt <= 30: # have to do this because arcgis is sketchy as hell and doesnt always come back while type(q)==list and query_cnt <= 30: # have to do this because arcgis is sketchy as hell and doesnt always come back
try: try:
print("querying {} layer...".format(name_list[i])) print(" querying {} layer...".format(name_list[i]))
query_starttime = time.time() query_starttime = time.time()
#Want to query where the geom filter aligns #Want to query where the geom filter aligns
...@@ -99,13 +99,13 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he ...@@ -99,13 +99,13 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
gis = GIS(url = 'https://virginiatech.maps.arcgis.com') gis = GIS(url = 'https://virginiatech.maps.arcgis.com')
lyr = FeatureLayer(url=url, gis=gis) lyr = FeatureLayer(url=url, gis=gis)
print("query time {}".format(query_endtime - query_starttime)) #print("query time {}".format(query_endtime - query_starttime))
if query_cnt > 30 and not q: if query_cnt > 30 and not q:
print("{} layer failed too many times, leaving empty".format(name_list[i])) print("{} layer failed too many times, leaving empty".format(name_list[i]))
continue continue
print("{} layer sucessfully queried".format(name_list[i])) print(" {} layer sucessfully queried".format(name_list[i]))
print("%s layer has %d points" %(name_list[i],len(q.features))) print(" %s layer has %d points" %(name_list[i],len(q.features)))
# re-build into list of x-y values # re-build into list of x-y values
# feat_points = [] # feat_points = []
query_dict = q.to_dict() query_dict = q.to_dict()
...@@ -200,7 +200,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he ...@@ -200,7 +200,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
y_pts_inac = test_pts[mask,1] y_pts_inac = test_pts[mask,1]
inac_endtime = time.time() inac_endtime = time.time()
print("{} inac took {}".format(j,inac_endtime - inac_starttime)) #print("{} inac took {}".format(j,inac_endtime - inac_starttime))
pts_inac = np.stack([x_pts_inac,y_pts_inac]).T pts_inac = np.stack([x_pts_inac,y_pts_inac]).T
...@@ -275,7 +275,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he ...@@ -275,7 +275,7 @@ def grab_features(anchor_point, extent, sample_dist = 10, case_name = 'blah', he
# save terrain as csv file (this method is pretty slow, but can compensate with interp) # save terrain as csv file (this method is pretty slow, but can compensate with interp)
if(get_elev): if(get_elev):
print("Getting terrain map, this will probably take a while....") print(" Getting terrain map, this will probably take a while....")
[e,e_interp,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point, [e,e_interp,x,y,data,ll_pt] = get_terrain_map(lat_lon=anchor_point,
sample_dist = sample_dist, sample_dist = sample_dist,
extent = extent, extent = extent,
......
lp_data/indexed_incidents.csv
View file @ 743c17e8
incident_index,area,IPP_lat,IPP_lon,find_lat,find_lon incident_index,area,IPP_lat,IPP_lon,find_lat,find_lon
1,0,31.39708335,-111.20643,31.406333,-111.19683 0,0,31.39708335,-111.20643,31.406333,-111.19683
2,1,31.42903334,-110.29328,31.429517,-110.30412 1,1,31.42903334,-110.29328,31.429517,-110.30412
3,2,31.50571667,-110.67622,31.51,-110.65333 2,2,31.50571667,-110.67622,31.51,-110.65333
4,3,31.72261664,-110.18783,31.741767,-110.19178 3,3,31.72261664,-110.18783,31.741767,-110.19178
5,4,31.83521665,-110.3567,31.862267,-110.39655 4,4,31.83521665,-110.3567,31.862267,-110.39655
6,5,31.90240002,-109.27847,31.8395,-109.27667 5,5,31.90240002,-109.27847,31.8395,-109.27667
7,6,31.92245,-109.96732,31.921483,-110.03358 6,6,31.92245,-109.96732,31.921483,-110.03358
8,7,32.01236667,-109.31572,31.99935,-109.3078 7,7,32.01236667,-109.31572,31.99935,-109.3078
9,8,32.33333333,-110.85283,32.3715,-110.86467 8,8,32.33333333,-110.85283,32.3715,-110.86467
10,8,32.33583333,-110.91017,32.3665,-110.88017 9,8,32.33583333,-110.91017,32.3665,-110.88017
11,8,32.33699999,-110.91667,32.359833,-110.89767 10,8,32.33699999,-110.91667,32.359833,-110.89767
12,9,32.40916665,-110.70983,32.400833,-110.69233 11,9,32.40916665,-110.70983,32.400833,-110.69233
13,9,32.41976668,-110.74733,32.3655,-110.77583 12,9,32.41976668,-110.74733,32.3655,-110.77583
14,9,32.43543332,-110.78933,32.428833,-110.79433 13,9,32.43543332,-110.78933,32.428833,-110.79433
15,10,32.82141666,-111.20212,32.824667,-111.22717 14,10,32.82141666,-111.20212,32.824667,-111.22717
16,11,33.37055,-111.11522,33.375283,-111.08918 15,11,33.37055,-111.11522,33.375283,-111.08918
17,12,33.38884999,-111.36565,33.4012,-111.37517 16,12,33.38884999,-111.36565,33.4012,-111.37517
18,12,33.39680001,-111.34805,33.4215,-111.3605 17,12,33.39680001,-111.34805,33.4215,-111.3605
19,12,33.39705,-111.34787,33.409333,-111.31717 18,12,33.39705,-111.34787,33.409333,-111.31717
20,12,33.39733334,-111.348,33.441667,-111.36767 19,12,33.39733334,-111.348,33.441667,-111.36767
21,12,33.39750001,-111.34783,33.415667,-111.365 20,12,33.39750001,-111.34783,33.415667,-111.365
22,13,33.47801666,-111.43767,33.415667,-111.36478 21,13,33.47801666,-111.43767,33.415667,-111.36478
23,14,33.60398331,-112.51512,33.590333,-112.52283 22,14,33.60398331,-112.51512,33.590333,-112.52283
24,15,33.70541668,-111.33805,33.67915,-111.31695 23,15,33.70541668,-111.33805,33.67915,-111.31695
25,16,34.0927,-111.42465,34.068767,-111.43592 24,16,34.0927,-111.42465,34.068767,-111.43592
26,16,34.09278333,-111.42098,34.102333,-111.49033 25,16,34.09278333,-111.42098,34.102333,-111.49033
27,17,34.55,-111.63333,34.55,-111.61667 26,17,34.55,-111.63333,34.55,-111.61667
28,17,34.55976664,-111.65395,34.540067,-111.70348 27,17,34.55976664,-111.65395,34.540067,-111.70348
29,18,34.6,-112.55,34.633333,-112.63333 28,18,34.6,-112.55,34.633333,-112.63333
30,18,34.63041668,-112.5553,34.627233,-112.54577 29,18,34.63041668,-112.5553,34.627233,-112.54577
31,19,34.82166665,-111.80667,34.818333,-111.79833 30,19,34.82166665,-111.80667,34.818333,-111.79833
32,19,34.82386665,-111.77513,34.831667,-111.743 31,19,34.82386665,-111.77513,34.831667,-111.743
33,19,34.86666667,-111.88333,34.866667,-111.8 32,19,34.86666667,-111.88333,34.866667,-111.8
34,19,34.88683332,-111.784,34.902667,-111.78683 33,19,34.88683332,-111.784,34.902667,-111.78683
35,19,34.89333331,-111.86333,34.906667,-111.87603 34,19,34.89333331,-111.86333,34.906667,-111.87603
36,20,34.90286668,-111.81313,34.891833,-111.80482 35,20,34.90286668,-111.81313,34.891833,-111.80482
37,20,34.91666667,-111.8,34.966667,-111.86667 36,20,34.91666667,-111.8,34.966667,-111.86667
38,20,34.92530003,-111.7341,34.897333,-111.74117 37,20,34.92530003,-111.7341,34.897333,-111.74117
39,21,34.97868334,-111.89643,34.946833,-111.88967 38,21,34.97868334,-111.89643,34.946833,-111.88967
40,22,35.1938,-114.05703,35.208717,-114.13195 39,22,35.1938,-114.05703,35.208717,-114.13195
41,23,35.24375,-111.59967,35.232517,-111.6015 40,23,35.24375,-111.59967,35.232517,-111.6015
42,24,35.33068333,-111.71108,35.334233,-111.6975 41,24,35.33068333,-111.71108,35.334233,-111.6975
43,25,36.23878333,-112.6892,36.246433,-112.70008 42,25,36.23878333,-112.6892,36.246433,-112.70008
44,26,41.54819,-80.33056,41.59006,-80.32647 43,26,41.54819,-80.33056,41.59006,-80.32647
45,27,42.0097,-74.42595,42.03544,-74.35565 44,27,42.0097,-74.42595,42.03544,-74.35565
46,27,42.02893,-74.33659,42.02902,-74.35191 45,27,42.02893,-74.33659,42.02902,-74.35191
47,28,42.17965,-74.21362,42.18345,-74.19585 46,28,42.17965,-74.21362,42.18345,-74.19585
48,29,42.31735,-76.47791,42.30271,-76.48976 47,29,42.31735,-76.47791,42.30271,-76.48976
49,30,42.34432,-77.47638,42.36077,-77.48593 48,30,42.34432,-77.47638,42.36077,-77.48593
50,31,42.74271,-73.45475,42.75163,-73.46259 49,31,42.74271,-73.45475,42.75163,-73.46259
51,32,43.06902,-74.48481,43.05475,-74.4839 50,32,43.06902,-74.48481,43.05475,-74.4839
52,33,43.42473,-73.73209,43.42878,-73.73981 51,33,43.42473,-73.73209,43.42878,-73.73981
53,34,43.42498,-74.41496,43.41592,-74.4142 52,34,43.42498,-74.41496,43.41592,-74.4142
54,34,43.42736,-74.4481,43.44347,-74.45004 53,34,43.42736,-74.4481,43.44347,-74.45004
55,34,43.4332,-74.41433,43.45684,-74.41519 54,34,43.4332,-74.41433,43.45684,-74.41519
56,34,43.4449,-74.4086,43.43407,-74.40747 55,34,43.4449,-74.4086,43.43407,-74.40747
57,35,43.51063,-74.57393,43.53014,-74.57169 56,35,43.51063,-74.57393,43.53014,-74.57169
58,36,43.65649,-76.00019,43.65566,-76.0115 57,36,43.65649,-76.00019,43.65566,-76.0115
59,37,43.73413,-74.25577,43.73701,-74.28305 58,37,43.73413,-74.25577,43.73701,-74.28305
60,38,43.8756,-74.43076,43.91132,-74.37437 59,38,43.8756,-74.43076,43.91132,-74.37437
61,39,43.95385,-75.15748,43.99774,-75.16148 60,39,43.95385,-75.15748,43.99774,-75.16148
62,40,44.16065,-73.85545,44.1547,-73.85942 61,40,44.16065,-73.85545,44.1547,-73.85942
63,41,44.19013,-74.81336,44.18772,-74.79719 62,41,44.19013,-74.81336,44.18772,-74.79719
64,42,44.28656,-74.61429,44.28923,-74.60208 63,42,44.28656,-74.61429,44.28923,-74.60208
65,43,48.1103,-121.4917,48.142,-121.4739 64,43,48.1103,-121.4917,48.142,-121.4739
lpm_hikersim.py
View file @ 743c17e8
import numpy as np import numpy as np
import matplotlib import matplotlib
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import os import os, sys, inspect
from os import path, getcwd from os import path, getcwd
import pandas as pd import pandas as pd
#Runs replicates of lost person model on data sets collected by lpm_maps.py currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0, parentdir)
from arcgis_terrain import get_terrain_map, lat_lon2meters
from LostPersonModel.main_hiker import run_replicate #change this import to where you have the LPM repo
#Runs replicates of lost person model on data sets collected by lpm_maps.py, producing a heatmap
#Bryson Howell, 7/24/24 #Bryson Howell, 7/24/24
def main(exp_name='test', n_envs=1, n_iter=10): def main(exp_name='test', n_envs=1, n_iter=10):
test_extent = 20000
download_extent = 40000
#Pull incident locations
listname = './lp_data/indexed_incidents.csv'
incidents = pd.read_csv(listname)
ipp_latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
find_latlons = incidents[["find_lat","find_lon"]].to_numpy(dtype=np.float32())
areas = incidents['area'].to_numpy(dtype=np.float32())
start = 0
n_envs = np.size(np.unique(areas))
for i in range(start, start+n_envs):
#Pull incident locations for environment
i_area = np.argwhere(areas==i)
i_area = np.transpose(i_area)[0]
s = 'Area {0} - indices are {1}'
lst = [i,i_area]
print(s.format(*lst))
#test - print start / end locations on grid
grid = np.zeros((3000,3000),dtype=np.int8())
for incident in range(0,len(i_area)):
#Dealing with large areas. Find the IPP point and resize around it
if(len(i_area) > 0):
for subzone in range(0,len(i_area)):
#First lat/lon in area is at the center
if(subzone == 0):
sub_center = ipp_latlons[i_area[incident]]
center_xy = lat_lon2meters(sub_center[0],sub_center[1])
#Determine position of other IPPs from the first
else:
cur_ipp = ipp_latlons[i_area[incident+subzone]]
ipp_xy = lat_lon2meters(cur_ipp[0],cur_ipp[1])
print(cur_ipp)
#Convert
#Locate find position from IPP
#Convert both to meters
#Use extent / cell count to determine which cell we're in:
return
#Set up which environments to run through.
start = 7 start = 7
n_envs = 1
if(n_envs == -1):
n_envs = np.size(np.unique(areas))
#Iterate through environment datasets
for i in range(start, start+n_envs): for i in range(start, start+n_envs):
dir = './map_layers/' + exp_name + '_' + str(i) + '/' dir = './map_layers/' + exp_name + '_' + str(i) + '/'
#Load datasets #Load datasets
...@@ -49,11 +111,21 @@ def main(exp_name='test', n_envs=1, n_iter=10): ...@@ -49,11 +111,21 @@ def main(exp_name='test', n_envs=1, n_iter=10):
map_data = (bw_inac, bw_lf, elev) map_data = (bw_inac, bw_lf, elev)
#Pull incident locations for environment #Pull incident locations for environment
listname = './lp_data/indexed_incidents.csv' i_area = np.argwhere(areas==i)
incidents = pd.read_csv(listname) i_area = np.transpose(i_area)[0]
latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32()) for incident in range(0,len(i_area)):
areas = incidents['area'].to_numpy(dtype=np.float32()) #s = 'Environment {0} - start {1} find {2}'
#s_lst = [i,]
print()
#For large downloads, re-size and center on IPP
#if(len(i_area) > 1)):
#for j in range
#we know initial point is at 0,0 but where is the final point...?
return return
......
lpm_maps.py
View file @ 743c17e8
...@@ -169,31 +169,17 @@ def investigate(): ...@@ -169,31 +169,17 @@ def investigate():
print(np.shape(elev)) #6000 x 6000. Got this using res of 25, not 10... print(np.shape(elev)) #6000 x 6000. Got this using res of 25, not 10...
print(elev[0][0]) print(elev[0][0])
#Make sure we can translate start / end grid cells...
return return
#From the set of LPM coordinates, download and save map/linear features for areas as necessary #Looks through a list of IPP and find locations for SAR incidents
#Also make images to show where areas are. #Groups IPPs that can fit within a map of extent_download size, such that a sub-map of extent_test size is centered on each IPP
def collect_terrain(folder_name='test',start_idx=0,max_collect=1): def group_IPP(listname, extent_download=40000, extent_test=20000):
extent_download = 40000 #Size of maps to dowload, in meters (big...)
extent_test = 20000 #Size of experiment area. 20km = 3000 grid cells
n_clusters = 10 #Number of areas to download for kmeans
count_thres = 3 #Minimum number of points needed for a large download (not used any more, hard to implement.)
max_down = max_collect #Maximum number of things to download. Set to -1 to download all environments
start_down = start_idx #Index to start downloading from
#Parameters for feature_set #Load in IPP and Find locations from csv file
res = 25 incidents = pd.read_csv(listname)
folder = folder_name
latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
ipp_x, ipp_y = lat_lon2meters(latlons[:,0],latlons[:,1])
#Collection of map centers, in meters. Use to check if we need to download a new area #Collection of map centers, in meters. Use to check if we need to download a new area
ipp_list = [] ipp_list = []
...@@ -202,48 +188,6 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1): ...@@ -202,48 +188,6 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1):
#Used to group search incidents in the same area together. Added to DF at end #Used to group search incidents in the same area together. Added to DF at end
keys = [] keys = []
listname = './lp_data/incident_locations.csv'
#samples = 1
#incidents = pd.read_csv(listname,nrows=samples)
incidents = pd.read_csv(listname)
samples = incidents.shape[0]
try_kmeans = 0
if(try_kmeans == 1):
latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
#latlons = np.concatenate(([incidents['IPP_lat']],[incidents['IPP_lon']]),axis=0)
print(latlons[0])
#latlons = whiten(latlons)
#clusters, distortions = kmeans(whiten(latlons),4)
clusters, distortions = kmeans(latlons,n_clusters)
plt.scatter(latlons[:,0],latlons[:,1])
plt.scatter(clusters[:,0],clusters[:,1],c='r')
plt.title('Lat/Lon Coordinates')
plt.show()
#Convert coordinates to x/y
clusters_x, clusters_y = lat_lon2meters(clusters[:,0],clusters[:,1])
ipp_x, ipp_y = lat_lon2meters(latlons[:,0],latlons[:,1])
print(clusters_x)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.scatter(ipp_x,ipp_y)
plt.scatter(clusters_x,clusters_y,c='r')
#try drawing boxes (in meters) around the areas we want to download
for i in range(0, len(clusters_x)):
rect = matplotlib.patches.Rectangle((clusters_x[i]-0.5*extent_download,clusters_y[i]-0.5*extent_download),extent_download,extent_download,facecolor="#86cecb",alpha=0.5)
ax.add_patch(rect)
plt.title('Meters Coordinates')
plt.show()
return
latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
ipp_x, ipp_y = lat_lon2meters(latlons[:,0],latlons[:,1])
#This gives a loose clustering algorithm, such that we know maps can be made from groups #This gives a loose clustering algorithm, such that we know maps can be made from groups
#Loop through search incidents #Loop through search incidents
for index, row in incidents.iterrows(): for index, row in incidents.iterrows():
...@@ -278,18 +222,17 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1): ...@@ -278,18 +222,17 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1):
else: else:
keys.append(closest) keys.append(closest)
print("Found %d maps to collect." % len(ipp_list)) print("Found %d maps to collect." % len(ipp_list))
if(max_collect < 0):
max_down = len(ipp_list)
print(keys) print(keys)
#Add group keys to dataframe #Add group keys to dataframe
incidents.insert(1,'area',keys) incidents.insert(1,'area',keys)
#Fix matlab indexing
incidents['incident_index'] = incidents['incident_index'] - 1
#Save new list as csv #Save new list as csv
new_incidents = True new_incidents = True
if(new_incidents): if(new_incidents):
incidents.to_csv('./lp_data/indexed_incidents.csv',index=False) incidents.to_csv('./lp_data/indexed_incidents.csv',index=False)
return
#For visualizing collected data on a map. #For visualizing collected data on a map. Todo: show world map as background (https://stackoverflow.com/questions/53233228/plot-latitude-longitude-from-csv-in-python-3-6)
draw = False draw = False
fig = plt.figure() fig = plt.figure()
ax = fig.add_subplot(111) ax = fig.add_subplot(111)
...@@ -307,20 +250,104 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1): ...@@ -307,20 +250,104 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1):
plt.title('Experiment Areas') plt.title('Experiment Areas')
plt.show() plt.show()
#Attempt to do kmeans to group areas by location.
#Problem is, this does not guarantee there is an area of test_extent size around each IPP
try_kmeans = 0
if(try_kmeans == 1):
latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
#latlons = np.concatenate(([incidents['IPP_lat']],[incidents['IPP_lon']]),axis=0)
print(latlons[0])
#latlons = whiten(latlons)
#clusters, distortions = kmeans(whiten(latlons),4)
clusters, distortions = kmeans(latlons,n_clusters)
plt.scatter(latlons[:,0],latlons[:,1])
plt.scatter(clusters[:,0],clusters[:,1],c='r')
plt.title('Lat/Lon Coordinates')
plt.show()
#Convert coordinates to x/y
clusters_x, clusters_y = lat_lon2meters(clusters[:,0],clusters[:,1])
ipp_x, ipp_y = lat_lon2meters(latlons[:,0],latlons[:,1])
print(clusters_x)
fig = plt.figure()
ax = fig.add_subplot(111)
plt.scatter(ipp_x,ipp_y)
plt.scatter(clusters_x,clusters_y,c='r')
#try drawing boxes (in meters) around the areas we want to download
for i in range(0, len(clusters_x)):
rect = matplotlib.patches.Rectangle((clusters_x[i]-0.5*extent_download,clusters_y[i]-0.5*extent_download),extent_download,extent_download,facecolor="#86cecb",alpha=0.5)
ax.add_patch(rect)
plt.title('Meters Coordinates')
plt.show()
return
return
#From the set of LPM coordinates, download and save map/linear features for areas as necessary
def collect_terrain(folder_name='test',start_idx=0,max_collect=1,format_ipps=False):
extent_download = 40000 #Size of maps to dowload, in meters (big...)
extent_test = 20000 #Size of experiment area. 20km = 3000 grid cells
n_clusters = 10 #Number of areas to download for kmeans
count_thres = 3 #Minimum number of points needed for a large download (not used any more, hard to implement.)
max_down = max_collect #Maximum number of things to download. Set to -1 to download all environments
start_down = start_idx #Index to start downloading from
#Parameters for feature_set
res = 25
folder = folder_name
#Load in IPP and Find locations from csv file
listname = './lp_data/indexed_incidents.csv'
incidents = pd.read_csv(listname)
ipp_latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
find_latlons = incidents[["find_lat","find_lon"]].to_numpy(dtype=np.float32())
areas = incidents['area'].to_numpy(dtype=np.float32())
#Limit what we download
if(max_down < 0):
max_down = np.size(np.unique(areas))
end_idx = start_idx + max_down
if(end_idx > np.size(np.unique(areas))):
end_idx = np.size(np.unique(areas))
#Decide if we want to save pics, and get the elev map (takes a while)
do_plot = True
do_elev = False
#Loop through unique areas to download
for i in range(start_idx, end_idx):
i_area = np.argwhere(areas==i)
i_area = np.transpose(i_area)[0]
#s = 'Area {0} - has points {1}'
#lst = [i,i_area]
#rint(s.format(*lst))
#Now download area centered on first index of area
print("Collecting GIS data %d/%d" % ((i-start_idx)+1,(end_idx-start_idx)))
#Download local map
if(len(i_area) > 0):
size = extent_test
else:
size = extent_download
ipp = ipp_latlons[i_area[0]]
s = ' Getting Area {0} with IPP(s) {1} - center is {2}'
lst = [i,i_area,ipp]
print(s.format(*lst))
folder_i = folder + '/' + folder + "_" + str(i)
grab_features(ipp, size, sample_dist = res,
case_name = folder_i, save_files = False, save_to_folder = True,
file_id = str(i), plot_data=do_plot, get_elev=do_elev)
print(" Saved terrain in %s" % folder_i)
return
#GIS is not working. Instead, look through matlab_data for matching IPP #Limit what we download
#matdir = '../ags_grabber/matlab_data/' if(max_down < 0):
#directory = os.fsencode(matdir) max_down = len(ipp_list)
#fname = 'BW_LFandInac-Zelev_[]'
#iterate through files in directory
#for file in os.listdir(directory):
#see if latitude matches
# for ilat in range(0,latlons.shape(0)):
# print(latlons[i][0])
#See if longitude matches
latlons = incidents[["IPP_lat","IPP_lon"]].to_numpy(dtype=np.float32())
#Now, if there's only one key in an area we can avoid downloading a lot. #Now, if there's only one key in an area we can avoid downloading a lot.
...@@ -339,8 +366,8 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1): ...@@ -339,8 +366,8 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1):
count = count + 1 count = count + 1
print('Getting initial point - ') print('Getting initial point - ')
print(ipp_lat_list[i]) print(ipp_lat_list[i])
folder = folder + "_" + str(i) folder_i = folder + "_" + str(i)
grab_features(ipp_lat_list[i], size, sample_dist = res, case_name = folder, save_files = False, save_to_folder = True, file_id = str(i), plot_data=do_plot, get_elev=do_elev) grab_features(ipp_lat_list[i], size, sample_dist = res, case_name = folder_i, save_files = False, save_to_folder = True, file_id = str(i), plot_data=do_plot, get_elev=do_elev)
return return
...@@ -360,23 +387,19 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1): ...@@ -360,23 +387,19 @@ def collect_terrain(folder_name='test',start_idx=0,max_collect=1):
#[e,e_interp,x,y,data,lat_lon] = get_terrain_map(ipp_point, sample_dist = res, extent = size, show_plot = True) #[e,e_interp,x,y,data,lat_lon] = get_terrain_map(ipp_point, sample_dist = res, extent = size, show_plot = True)
#Load Linear Features. Probably need to convert some Matlab code...
#return
#Collect GIS maps and layers for SAR initial positions #Collect GIS maps and layers for SAR initial positions
def main(): def main():
listname = './lp_data/incident_locations.csv' #Collection of search incidents
extent = 3000 #size in meters of map area
#incident_list() #incident_list()
#gis_test() #gis_test()
collect_terrain(folder_name='big',start_idx=7,max_collect=1) #group_IPP(listname) #Format incident data correctly
#Friday, 7/26 - make sure we get window size of 625 with larger environment.
#Also, try reducing size of downloads...
collect_terrain(folder_name='test',start_idx=0,max_collect=1)
#investigate() #investigate()
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment